Undirected Graphical Models as Approximate Posteriors

Understand

The representation of the approximate posterior is a critical aspect of effective variational autoencoders (VAEs).

• Poor choices for the approximate posterior have a detrimental impact on the generative performance of VAEs due to the mismatch with the true posterior.
• We extend the class of posterior models that may be learned by using undirected graphical models.
• We develop an efficient method to train undirected approximate posteriors by showing that the gradient of the training objective with respect to the parameters of the undirected posterior can be computed by backpropagation through Markov chain Monte Carlo updates.